High voltage systems (tube-type microwave)/low voltage system (solid-state microwave) power distribution

SPS satellite power distribution systems are described. The reference Satellite Power System (SPS) concept utilizes high-voltage klystrons to convert the onboard satellite power from dc to RF for transmission to the ground receiving station. The solar array generates this required high voltage and the power is delivered to the klystrons through a power distribution subsystem. An array switching of solar cell submodules is used to maintain bus voltage regulation. Individual klystron dc voltage conversion is performed by centralized converters. The on-board data processing system performs the necessary switching of submodules to maintain voltage regulation. Electrical power output from the solar panels is fed via switch gears into feeder buses and then into main distribution buses to the antenna. Power also is distributed to batteries so that critical functions can be provided through solar eclipses

Relationship of components of forensic service users' experience of recovery

Section A A literature review considering the role of interpersonal relationships in forensic service users’ accounts of recovery. A systematic literature search identified twenty studies with qualitative descriptions of forensic service user recovery experience. These are critiqued and synthesised using an integrative review process. Results are presented under four resulting categories: relationships with staff, relationships with service user peers, relationships with family and friends and relationships with the wider community. Findings suggest that interpersonal relationships play an important role in recovery for forensic service users and highlight the relevance of a relational model in service provision. Clinical and research implications are discussed. Section B A qualitative study using Grounded Theory methodology to construct an understanding of the psychological and relational processes found within a forensic service user reflective group. Interviews were conducted with both service user and staff facilitator attendees of a reflective group run on a medium secure forensic ward. Results formed a flexible, cyclical model based around four key categories: ‘Group Identity’, ‘Linking Self with Others’, ‘The Changing Self’ and ‘Living Visibly in a System’. Findings are presented as providing a solid rationale for the inclusion of service user reflective groups in forensic inpatient settings. Discussion of how this model contributes to and is complemented by existing theory is presented and clinical/research implications suggested

ART Neural Networks for Remote Sensing Image Analysis

ART and ARTMAP neural networks for adaptive recognition and prediction have been applied to a variety of problems, including automatic mapping from remote sensing satellite measurements, parts design retrieval at the Boeing Company, medical database prediction, and robot vision. This paper features a self-contained introduction to ART and ARTMAP dynamics. An application of these networks to image processing is illustrated by means of a remote sensing example. The basic ART and ARTMAP networks feature winner-take-all (WTA) competitive coding, which groups inputs into discrete recognition categories. WTA coding in these networks enables fast learning, which allows the network to encode important rare cases but which may lead to inefficient category proliferation with noisy training inputs. This problem is partially solved by ART-EMAP, which use WTA coding for learning but distributed category representations for test-set prediction. Recently developed ART models (dART and dARTMAP) retain stable coding, recognition, and prediction, but allow arbitrarily distributed category representation during learning as well as performance

Art Neural Networks for Remote Sensing: Vegetation Classification from Landsat TM and Terrain Data

A new methodology for automatic mapping from Landsat Thematic Mapper (TM) and terrain data, based on the fuzzy ARTMAP neural network, is developed. System capabilities are tested on a challenging remote sensing classification problem, using spectral and terrain features for vegetation classification in the Cleveland National Forest. After training at the pixel level, system performance is tested at the stand level, using sites not seen during training. Results are compared to those of maximum likelihood classifiers, as well as back propagation neural networks and K Nearest Neighbor algorithms. ARTMAP dynamics are fast, stable, and scalable, overcoming common limitations of back propagation, which did not give satisfactory performance. Best results are obtained using a hybrid system based on a convex combination of fuzzy ARTMAP and maximum likelihood predictions. A prototype remote sensing example introduces each aspect of data processing and fuzzy ARTMAP classification. The example shows how the network automatically constructs a minimal number of recognition categories to meet accuracy criteria. A voting strategy improves prediction and assigns confidence estimates by training the system several times on different orderings of an input set.National Science Foundation (IRI 94-01659, SBR 93-00633); Office of Naval Research (N00014-95-l-0409, N00014-95-0657

The Obsolescence of Advertising in the Information Age

The vast amount of product information available to consumers through online search renders most advertising obsolete as a tool for conveying product information. Advertising remains useful to firms only as a tool for persuading consumers to purchase advertised products. In the mid-twentieth century, courts applying the antitrust laws held that such persuasive advertising is anticompetitive and harmful to consumers, but the Federal Trade Commission (FTC) was unable to pursue an antitrust campaign against persuasive advertising for fear of depriving consumers of advertising\u27s information value. Now that the information function of most advertising is obsolete, the FTC should renew its campaign against persuasive advertising by treating all advertising beyond the minimum required to ensure that product information is available to online searchers as monopolization in violation of section 2 of the Sherman Act

Big Data, Price Discrimination, and Antitrust

Antitrust law today guarantees a particular distribution of wealth between consumers and firms by promoting competition in some markets, but allowing firms to retain pricing power in other markets, such as those in which a firm has achieved power through oligopoly or by fielding a superior product. By giving firms the power to identify individual consumers at the point of sale and determine the maximum price that each consumer can be made to pay for a product, big data will soon allow firms with pricing power to charge each consumer the highest price that the consumer is able to pay, upending the current distribution of wealth. Current antitrust rules cannot respond because those rules determine the distribution of wealth only indirectly, through regulation of competition, instead of directly through the regulation of prices, leaving firms with pricing power free to use their data to raise prices. As a political matter, a response will be necessary, however, because consumers will rebel against attempts to diminish their wealth. Two options preserve the current distribution of wealth. One is to change antitrust rules to require more competition in markets that are exempt from antitrust scrutiny today. The traditional objection to such a deconcentration campaign, that it might reduce rewards to firms for innovation, would not apply because the purpose of deconcentration here would be to restore the current, presumably sufficiently rewarding, distribution of wealth. The other option is use by government of big data to set prices designed to maintain the current distribution of wealth. Big data would make price regulation of this kind possible by allowing regulators to calculate precisely how much wealth a given pricing policy lets consumers retain in a given market. One advantage of price regulation over deconcentration is that regulators would be able to use big data to tailor prices to achieve social justice ends, such as ensuring that the neediest consumers obtain the most value from their purchases